Abe D, Inaji M, Hase T, Takahashi S, Sakai R, Ayabe F, Tanaka Y, Otomo Y, Maehara T. A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms. JAMA network open. 2022.06; 5 (6): e2216393. ( PubMed, DOI )

Polouliakh N, Hase T, Ghosh S, Kitano H. Toxicity Analysis of Pentachlorophenol Data with a Bioinformatics Tool Set. Methods in molecular biology (Clifton, N.J.). 2022; 2486 105-125. ( PubMed, DOI )

Katsuda T, Sato N, Mogushi K, Hase T, Muramatsu M. Sub-GOFA: A tool for Sub-Gene Ontology function analysis in clonal mosaicism using semantic (logical) similarity. Bioinformation. 2022; 18 (1): 53-60. ( PubMed, DOI )

(*Takeshi Hase is a part of, FANTOM consortium), Grapotte M, Saraswat M, Bessière C, Menichelli C, Ramilowski JA, Severin J, Hayashizaki Y, Itoh M, Tagami M, Murata M, Kojima-Ishiyama M, Noma S, Noguchi S, Kasukawa T, Hasegawa A, Suzuki H, Nishiyori-Sueki H, Frith MC, * FANTOM consortium, Chatelain C, Carninci P, de Hoon MJL, Wasserman WW, Bréhélin L, Lecellier CH. Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network Nature Communications. 2021.06; 12, 3297. ( DOI )

Nishimura T, Nakamura H, Yachie A, Hase T, Fujii K, Koizumi H, Naruki S, Takagi M, Matsuoka Y, Furuya N, Kato H, Saji H. Disease-related cellular protein networks differentially affected under different EGFR mutations in lung adenocarcinoma. Scientific reports. 2020.12; 10 (1): 10881. ( PubMed, DOI )

(Book Chapter) "Chapter 13. Cancer Network Medicine". - Network Medicine: Complex Systems in Human Disease and Therapeutics (Joseph Loscalzo, Albert-László Barabási, and Edwin K. Silverman (Eds.)) 2017.12; 294-323. ( DOI )

Caron E, Roncagalli R, Hase T, Wolski WE, Choi M, Menoita MG, Durand S, García-Blesa A, Fierro-Monti I, Sajic T, Heusel M, Weiss T, Malissen M, Schlapbach R, Collins BC, Ghosh S, Kitano H, Aebersold R, Malissen B, Gstaiger M. Precise Temporal Profiling of Signaling Complexes in Primary Cells Using SWATH Mass Spectrometry. Cell reports. 2017.03; 18 (13): 3219-3226. ( PubMed, DOI )

Hu Y, Hase T, Li HP, Prabhakar S, Kitano H, Ng SK, Ghosh S, Wee LJ. A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data. BMC genomics. 2016.12; 17 (Suppl 13): 1025-29. ( PubMed, DOI )

Effect of placebo and lorazepam on functional connectivity in fearful vocal processing: an fMRI study 2016.06; 19 (Suppl_1): 54. ( DOI )

Inferring causal molecular networks: empirical assessment through a community-based effort 2016.04; 13 (4): 310-318. ( DOI )

Hase T, Kikuchi K, Ghosh S, Kitano H, Tanaka H. A computational approach to prioritize drug-target genes in the human protein interaction network The proceedings of The 21st International Symposium on Artificial Life and Robotics 2016 (AROB 21st 2016). 2016.01; 871-876.

Kawakami E, Singh VK, Matsubara K, Ishii T, Matsuoka Y, Hase T, Kulkarni P, Siddiqui K, Kodilkar J, Danve N, Subramanian I, Katoh M, Shimizu-Yoshida Y, Ghosh S, Jere A, Kitano H. Network analyses based on comprehensive molecular interaction maps reveal robust control structures in yeast stress response pathways. NPJ systems biology and applications. 2016; 2 15018. ( PubMed, DOI )

Hase T, Kikuchi K, Ghosh S, Kitano H, Tanaka H. Controllability of protein-protein interaction networks and their relationships with drug-targets, essential genes, and degree connectivities. The proceedings of The 20th International Symposium on Artificial Life and Robotics 2015 (AROB 20th 2015). 2015.01; 813-818.

Takeshi Hase, Kaito Kikuchi, Samik Ghosh, Hiroaki Kitano, Hiroshi Tanaka. (Research article) Identification of drug-target modules in the human protein–protein interaction network Artificial Life and Robotics. 2014.12; 19 (4): 406-413. ( DOI )

Matsuoka Y, Matsumae H, Katoh M, Eisfeld AJ, Neumann G, Hase T, Ghosh S, Shoemaker JE, Lopes TJ, Watanabe T, Watanabe S, Fukuyama S, Kitano H, Kawaoka Y. A comprehensive map of the influenza A virus replication cycle. BMC systems biology. 2013.10; 7 (97): 97. ( PubMed, DOI )

Hase T, Ghosh S, Yamanaka R, Kitano H. Harnessing diversity towards the reconstructing of large scale gene regulatory networks. PLoS computational biology. 2013; 9 (11): e1003361. ( PubMed, DOI )

(Book Chapter) "Chapter 20. Protein-Protein Interaction Networks: Structures, Evolution, and Application to Drug Design" - Protein-Protein Interactions - Computational and Experimental Tools (W. Cai, H. Hong (eds.)) 2012.03; 405-426. ( DOI )

Hanada K, Hase T, Toyoda T, Shinozaki K, Okamoto M. Origin and evolution of genes related to ABA metabolism and its signaling pathways. Journal of plant research. 2011.07; 124 (4): 455-65. ( PubMed, DOI )

Hase T, Niimura Y, Tanaka H. Difference in gene duplicability may explain the difference in overall structure of protein-protein interaction networks among eukaryotes. BMC evolutionary biology. 2010.11; 10 358. ( PubMed, DOI )

Hase T, Tanaka H, Suzuki Y, Nakagawa S, Kitano H. Structure of protein interaction networks and their implications on drug design. PLoS computational biology. 2009.10; 5 (10): e1000550. ( PubMed, DOI )

Hase T, Niimura Y, Kaminuma T, Tanaka H. Non-uniform survival rate of heterodimerization links in the evolution of the yeast protein-protein interaction network. PloS one. 2008.02; 3 (2): e1667. ( PubMed, DOI )

長谷 武志, 荻島 創一, 中川 草, 田中 博. 1P301 タンパク質間相互作用ネットワークのトポロジー構造における構造決定因子について(数理生物学)) 生物物理. 2005; 45 S107. ( DOI )

長谷 武志. (招待講演) Lecture 05: Application of machine learning methods to drug discovery. 1st Big Data Machine Learning in Healthcare in Japan 2018.02.25

Takashi Hase, Shingo Tsuji, Kazuro Shimokawa, Hiroshi Tanaka. (Peer reviewed) Application of Deep Learning to Drug Discovery. Workshop on Artificial Life and Robotics in Busan 2017.09.08

辻 真吾, 長谷 武志, 田中 博, 油谷 浩幸. タンパク相互作用ネットワークを用いた新しいがん治療のためのAI創薬. 日本癌学会総会記事 2017.09.01

Takeshi Hase, Samik Ghosh, Ayako Yachie, Hiroaki Kitano. (Peer reviewed) A neural network based text mining approach for inference of protein-protein interaction networks.. 2nd International Symposium on BioComplexity 2017.01.20 B-Con PLAZA, Beppu, JAPAN

Yongli Hu, Takeshi Hase, Huipeng Li, Shyam Prabhakar, Hiroaki Kitano, See Kiong Ng, Samik Ghosh, Lawrence Jin, Kiat Wee. (Peer-reviewed) A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.. 15th International Conference on Bioinformatics (InCOB 2016) 2016.09.21 Singapore

Takeshi Hase, Kaito Kikuchi, Samik Ghosh, Hiroshi Tanaka, Hiroaki Kitano. (Peer reviewed) A computational approach to prioritize drug-target genes in the human protein interaction network. 1st International Symposium on BioComplexity 2016.01.21 B-Con PLAZA, Beppu, JAPAN

Takeshi Hase, Kaito Kikuchi, Samik Ghosh, Hiroaki Kitano, Hiroshi Tanaka. (peer-reviewed) Controllability of protein-protein interaction networks and their relationships with drug-targets, essential genes, and degree connectivities. International Symposium on Artificial Life and Robotics AROB 20th 2015 2015.01.21

Kaito Kikuchi, Takeshi Hase, Samik Ghosh, Hiroaki Kitano. (peer-reviewed) A network guided approach towards identification of novel drug targets in MRSA. 8th Asian Young Researchers Conference on Computational and Omics Biology (AYRCOB 2015.01.19

Takeshi Hase. Identification of drug-target modules in the human protein–protein interaction network. AROB 19th 2014.01.22 Oita, Japan

Takeshi Hase, Kaito Kikuchi, Samik Ghosh, Hiroaki Kitano, Hiroshi Tanaka. (peer-reviewed) Identification of drug-target modules in the human protein–protein interaction network. International Symposium on Artificial Life and Robotics 2014.01.01 B-Con Plaza, Beppu, Japan

一般口演, Takeshi Hase, Yoshihito Niimura. (Peer-reviewed) Difference in gene duplicability may explain the difference in overall structure of protein-protein interaction networks among eukaryotes.. Society for Molecular Biology and Evolution 2012 2012.06.01 Dublin Ireland